Large-Scale Semantic Exploration of Scientific Literature using Topic-based Hashing Algorithms

Tracking #: 2239-3452

This paper is currently under review
Carlos Badenes-Olmedo
José Luís Redondo-García
Oscar Corcho

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Guest Editors Semantic E-Science 2018

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Full Paper
Searching for similar documents and exploring major themes covered across groups of documents are common actions when browsing collections of scientific papers. This manual, knowledge-intensive task may become less tedious and even lead to unforeseen relevant findings if unsupervised algorithms are applied to help researchers. Most text mining algorithms represent documents in a common feature space that abstracts away from the specific sequence of words used in them. Probabilistic Topic Models reduce that feature space by annotating documents with thematic information. Over this low-dimensional latent space some locality-sensitive hashing algorithms have been proposed to perform document similarity search. However, thematic information is hidden behind hash codes, preventing thematic exploration and limiting the explanatory capability of topics to justify content-based similarities. This paper presents a novel hashing algorithm based on approximate nearest-neighbor techniques that uses hierarchical sets of topics as hash codes. It not only performs efficient similarity searches, but also allows extending those queries with thematic restrictions explaining the similarity score from the most relevant topics. Extensive evaluations on both scientific and industrial text datasets validate the proposed algorithm in terms of accuracy and efficiency.
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